Advanced multi-sensor processing

ABSTRACT

Each receiving node ( 120 ) of a plurality of receiving nodes ( 120 - 1, 120 - 2  and  120 - 3 ) such as base stations in a wireless network converts a superposition of signals received from a plurality of transmitting nodes such as mobile terminals ( 10 ) to produce soft complex signal information. The soft complex signal information associated with the considered plurality of receiving nodes are collected, for example in a central node ( 130 ) and jointly detect signal information transmitted from at least a subset of the plurality of transmitting nodes ( 10 ) based on the collected soft complex signal information. The collected soft signal information generally retains phase and amplitude information, and the transmitted signals are preferably detected in a joint detection process based on a complex channel representation and collected soft signal information. In a truly distributed realization, adjacent receiving nodes or base stations exchange soft complex signal information with each other, thus forming at least partially overlapping groups for distributed collection of information, detection and subsequent decoding in each base station.

This application is the US national phase of international applicationPCT/EP2004/052701, filed 28 Oct. 2004, which designated the U.S. andclaims priority of EP 03104952.1, filed 23 Dec. 2003, the entirecontents of each of which are hereby incorporated by reference.

TECHNICAL FIELD OF THE INVENTION

The present invention generally concerns wireless communication networkssuch as digital cellular networks, and especially uplink signalprocessing and detection and decoding in wireless networks.

BACKGROUND

One way of enhancing network performance is to utilize signals frommultiple sensors or antennas in the network. There are a variety ofexisting techniques for exploiting signals from multiple sensors orantennas, especially in relation to the uplink in a cellular network.

Advanced antenna solutions such as adaptive antenna systems and MIMO(Multiple-Input Multiple-Output) systems can be used to enhance systemperformance. Space-time coding as represented by references [1], [2] and[3] can be regarded as a method for providing diversity for a wirelessfading channel using multiple transmit and/or receive antennas. FIG. 1schematically illustrates an example of a classical MIMO system, where atransmitting node 10 has multiple m transmit antennas and a receivingnode 20 has multiple n receive antennas. In matrix form, the channelmodel can be expressed as:y=Hx+wx=G(c ₁ , . . . ,c _(p)),where y is the received signal vector, H is a n by m complex channelmatrix, x is the transmitted signal vector, w is a vector representationof white noise, G is a code matrix and c is a symbol in a code book, andp is the number of symbols per block. The complex channel gain matrix Hcan be written as:

$H = \begin{bmatrix}h_{11} & \ldots & h_{1m} \\\vdots & ⋰ & \vdots \\h_{n\; 1} & \ldots & h_{nm}\end{bmatrix}$where h_(ij) is the complex channel gain from transmit antenna j in thetransmitting node to receive antenna i in the receiving node.

Soft handover is an entirely different method of exploiting so-calledmulti-sensor information, now further up in the network at a combiningpoint and based on information from multiple base stations. In softhandover, the signal from a mobile terminal is received by two or morebase stations, which transfer respective decoded data to an RNC (RadioNetwork Controller) for combining.

Softer handover in WCDMA refers to the situation when a mobile terminalis in the overlapping coverage area of two or more adjacent sectors of abase station, where the signal from the mobile is received by eachsector, and then transferred to the same RAKE receiver for maximum ratiocombining.

In practice, WCDMA (Wideband Code Division Multiple Access) normallyemploys a rather “hard” handover known as macro selection diversityrather than ideal soft handover. This typically means that some qualityor reliability indicator, such as a CRC checksum, received pilot signalstrength or a frame reliability indicator, is used for enabling dynamicselection of the better data and/or frame from the base stations.

FIG. 2 schematically illustrates uplink diversity in a WCDMA system, inwhich a mobile terminal 10 establishes radio links with multiple basestations (or node Bs) 20-1 and 20- and/or sectors simultaneously. Softerhandover, also referred to as intersector diversity, here involves thereception of signals from the mobile terminal at different sectorswithin the same base station 20 followed by maximum ratio combining(MRC) on soft baseband signals in the MRC combiner 22 prior to channeldecoding in the channel decoder 24.

Soft handover, also referred to as intercell site diversity, typicallyinvolves the transmission of hard decision data after channel decoding,together with associated reliability information, from multiple basestations 20-1 and 20-2 to the RNC (Radio Network Controller) 30 forper-user selection combining of the decoded data according to thereliability information, for example as described in reference [4].

Ideal soft handover operates on soft baseband signals that aretransferred from the base stations to a combining point for maximumratio combining or similar combining per-user (when noise andinterference from different base stations are uncorrelated), for exampleas described in references [5] and [6].

Reference [5] presents an uplink protocol based on the multiple-to-onerelationship between base stations and mobile. As illustrated in FIG. 3,the uplink protocol involves transferring non-decoded quantizedinformation from a number of receiving base stations 20-1 and 20-2 to aso-called controlling base station 20-3. The controlling base station20-3 then employs majority combining, maximum ratio combining or maximumprobability combining of the received quantized information for optimaldecoding of the mobile 10.

Reference [6] is also related to the multiple-to-one relationshipbetween base stations and mobile, and concerns the situation of severalbase stations receiving a signal from a mobile terminal and forwardinginformation to a central exchange node for decoding of the mobile.

Common to all known soft handover is that per-user combining is employedand that interference from other mobile terminals is generally treatedas unstructured noise, thus failing to optimally reflect and considerthe actual situation at the receiving base stations.

RELATED ART

Reference [7] relates to Linear Minimum Mean Square Error (LMMSE)receivers capable of suppressing multiple access interference andnear-far occurrences in a CDMA system operating in multi-path fadingwireless channels.

Reference [8] is a recently published doctoral thesis on the subject ofsoft detection and decoding in WCDMA systems.

SUMMARY OF THE INVENTION

The technology disclosed herein overcomes these and other drawbacks ofthe prior art arrangements.

It is a general object of the technology disclosed herein to improve theperformance of a wireless communication network such as a digitalcellular network.

It is an object of the technology disclosed herein to more optimallyexploit signals from multiple base stations or similar receiving nodesin a wireless network. In particular, it is desirable to improve theuplink signal processing in a cellular network.

Yet another object of the technology disclosed herein is to find a wayto keep the costs for transporting the data required for the purpose ofuplink signal processing at a reasonable level.

It is a particular object to provide a method and system for detectionof signal information in a wireless communication network.

It is also an object of the technology disclosed herein to provide anetwork node for signal detection in a wireless communication network.

The technology disclosed herein considers a plurality of receiving nodessuch as base stations in a wireless network. Each receiving nodeconverts a superposition of signals received from a plurality oftransmitting nodes such as mobile terminals to produce soft complexsignal information. A basic idea according to the technology disclosedherein is to collect soft complex signal information associated with theconsidered plurality of receiving nodes over a transport network, andjointly detect signal information transmitted from at least a subset ofthe plurality of transmitting nodes based on the collected soft complexsignal information. The collected soft signal information generallyretains phase and amplitude information, and the transmitted signals arepreferably detected in a joint detection process based on a complexchannel representation and the collected soft signal information.

The soft signal information is usually represented by soft complexbaseband signals, although any other type of soft information retainingphase and amplitude information may be utilized by the invention.Complex samples can always be represented by a real and imaginarycomponent (rectangular coordinate system), or equivalently, by amplitudeand phase (polar coordinate system). Soft information generally has ahigher information content than the detected or decoded information, andis usually represented by multiple (often binary) digits per signalcomponent.

Instead of per-user combining, the technology disclosed herein providesjoint detection of a plurality of transmitting nodes or mobiles. Thetechnology disclosed herein does not treat interference from othertransmitting nodes as unstructured noise, in clear contrast to cellularsystems of today. In effect, the signal processing approach suggested bythe technology disclosed herein rather strives to cancel suchinterference.

The process of jointly detecting signal information is preferably basedon the collected soft complex signal information and a complex channelrepresentation related to the plurality of considered receiving andtransmitting nodes. The complex channel representation is preferablyrepresented by a complex channel gain matrix.

In a practical realization, a complex channel gain matrix may bedetermined by explicit channel estimation. Alternatively, differentcombinations of complex channel gain matrix and symbol hypothesis vectormay be tested in a joint search procedure to find an optimal symbolhypothesis vector that will then represent the detected signalinformation. Any general detection algorithm, such as Zero Forcing (ZF),Maximum Likelihood Detection-Multi-User Detection (MLD-MUD) and LinearMinimum Mean Squared Error (LMMSE), may be used by the technologydisclosed herein. Once detected, the signal information may be used as abasis for subsequent decoding processes such as error correctiondecoding and source decoding. Optionally, the decoding process can beconsidered as an integrated part of the detection process, e.g. by usingmulti-user based decoding. This means that detection can be done per bitor symbol or per sequence of bits or symbols, for multiple users.

The main benefit of this approach over other state-of-the-art techniquesis that it enables/offers the optimal formulation for uplink signalprocessing, especially if all nodes in the wireless network are underconsideration in a centralized approach.

In the centralized approach, soft complex signal information iscollected from the considered receiving nodes and processed in a centralnode. Although optimal from a signal processing point of view, thecentralized multi-sensor processing approach may lead to somewhat highertransport costs for the network operators because of the large amountsof information that may have to be transported relatively long distances(depending on the size of the network).

Therefore, the technology disclosed herein also proposes a distributedapproach to the novel multi-sensor processing scheme. The distributedapproach is based on partitioning receiving nodes into multiple groups,and collecting, for each group, soft complex signal informationassociated with the receiving nodes of the group, and finally performinggroup-wise joint detection based on the collected information. Moreparticularly, on group level, the joint detection is preferablyperformed based on the collected soft complex signal informationassociated with the considered group and a complex channel gainsub-matrix related to the receiving nodes of the group and the relevanttransmitting nodes. The rationale is that interference only has alimited meaning at very far distances, and hence it makes little senseto distribute soft baseband information outside a rather localneighborhood.

In a truly distributed realization, adjacent receiving nodes or basestations exchange soft complex signal information with each other, thusforming at least partially overlapping groups for distributed collectionof information, detection and subsequent decoding in each base station.Alternatively, the task of collecting soft complex information andperforming joint detection and optionally also decoding may be assignedto a signal processing node that is associated with the group. Such asignal processing node may of course be a designated base station thatbelongs to the corresponding group.

In order to avoid multiple copies of the same decoded information toegress the network, decoded information may be transported to a (hard)combining point, where higher layer protocols such as ARQ (AutomaticRepeat ReQuest) can be handled

The performance of the distributed approach will be asymptotically closeto the centralized multi-sensor processing even for relatively smallgroups involving just a few base stations, and also means that softinformation only have to be transported within a local neighborhood.Shorter transport distances in the transport network generally meansreduced costs for the operators.

The technology disclosed herein also provides a procedure for performingiterative detection of signal information based on distributedsuccessive interference cancellation.

It has also been recognized that the amount of information that need tobe transported over the transport network can be significantly reducedby compressing soft complex signal information before it is transportedover the transport network and subsequently de-compressing thecompressed soft complex information so that it can be fully exploited inthe detection and decoding process.

The technology disclosed herein offers the following advantages:

-   -   Improved network performance;    -   Optimal formulation for uplink signal processing in a digital        cellular network;    -   More optimal exploitation of signals from multiple base        stations;    -   Alternatives for reducing the costs for transporting soft        complex signal information (distributed approach and/or        compression);    -   Integrated interference cancellation in the uplink signal        processing; and    -   Reduced transmit power consumption, since transmit power can be        controlled with reference to the noise floor (as interference is        cancelled to a large extent).

Other advantages offered by the present technology disclosed herein willbe appreciated upon reading of the below description of the exampleembodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology disclosed herein, together with further objects andadvantages thereof, will be best understood by reference to thefollowing description taken together with the accompanying drawings, inwhich:

FIG. 1 is a schematic diagram illustrating an example of a classicalMIMO system;

FIG. 2 is a schematic diagram illustrating uplink diversity in a priorart WCDMA system;

FIG. 3 is a schematic diagram illustrating a prior art uplink protocolbased on the multiple-to-one relationship between base stations andmobile;

FIG. 4 is a schematic diagram illustrating an example of a centralizedarchitecture and signal processing approach according to a preferredexample embodiment;

FIG. 5 is a schematic flow diagram illustrating a method according to apreferred example embodiment;

FIG. 6 is a schematic block diagram illustrating an example of apreferred realization for multi-sensor processing according to anexample embodiment;

FIG. 7 is a schematic diagram illustrating an example of detection anddecoding unit according to an exemplary example embodiment;

FIG. 8 is a schematic diagram illustrating an exemplary architecture andsignal processing approach according to an alternative exampleembodiment;

FIG. 9 is a schematic diagram illustrating an example of a distributedarchitecture and signal processing approach according to a preferredexample embodiment;

FIG. 10 illustrates an example of a distributed architecture and signalprocessing approach according to an alternative example embodiment;

FIG. 11 is a schematic diagram illustrating an example of the signalexchange in a distributed realization with optional successiveinterference cancellation according to a preferred example embodiment;

FIG. 12 is a schematic block diagram illustrating a realization ofmulti-sensor processing including compression and de-compression of softinformation according to an exemplary example embodiment; and

FIG. 13 illustrates power control and link mode feedback in a systemaccording to an exemplary example embodiment.

DETAILED DESCRIPTION

Throughout the drawings, the same reference characters will be used forcorresponding or similar elements.

FIG. 4 is a schematic diagram illustrating an example of a centralizedsystem architecture and signal processing approach according to apreferred example embodiment. The network comprises a plurality ofreceiving nodes 120-1, 120-2, 120-3 such as base stations and aplurality of transmitting nodes 10 such as mobile terminals in awireless network. Each receiving node 120 converts a superposition ofsignals received from a plurality of transmitting nodes 10 to producesoft complex signal information, and forwards soft complex signalinformation to a central node 130, typically over a transport network.The central node 130 may be a dedicated network node or implemented inan RNC (Radio Network Controller), BSC (Base Station Controller) or SHOD(Soft Handover Device). The central node 130 jointly detects signalinformation from the plurality of transmitting nodes based the collectedsoft signal information, and typically performs subsequent decoding,such as error correction decoding and/or source decoding, based on thedetected signal information.

The term detection shall however be interpreted in a broad sense.Detection can take place on bit level, symbol level or on sequences ofbits or symbols. Detection may take place on coded information or oninformation bits. The former means that decoding is generally performedon a per-user basis after detection, whereas the latter means that thedecoding is integrated and then performed on multiple users. As will beappreciated below, the technology disclosed herein can also beimplemented with successive or parallel interference cancellation.

The main benefit of this approach over other state-of-the-art techniquesis that it enables/offers the optimal formulation for uplink signalprocessing, especially if all nodes in the wireless network are underconsideration in a centralized approach. Instead of per-user combining,the technology disclosed herein provides joint detection of a pluralityof transmitting nodes or mobiles. The invention does not treatinterference from other transmitting nodes as unstructured noise, inclear contrast to cellular systems of today. In effect, the signalprocessing approach suggested by the technology disclosed herein ratherstrives to cancel such interference.

The optimal formulation for joint detection on the “uplink”, assuming Mtransmitting nodes and N receiving nodes, is preferably written in thefrequency domain as:

$\begin{bmatrix}R_{1} \\\vdots \\R_{N}\end{bmatrix} = {{\begin{bmatrix}H_{11} & \ldots & H_{1M} \\\vdots & ⋰ & \vdots \\H_{N\; 1} & \ldots & H_{NM}\end{bmatrix} \cdot \begin{bmatrix}S_{1} \\\vdots \\S_{M}\end{bmatrix}} + \begin{bmatrix}N_{1} \\\vdots \\N_{N}\end{bmatrix}}$where R_(i) represents the soft complex information from receiving nodei, H_(ij) represents a complex estimate (including fading and phaseshift information) of the channel from transmitting node j to receivingnode i, S_(j) represents the signal transmitted from transmitting node jand N_(j) is a representation of white complex Gaussian channel noiseassociated with receiving node j. In the following, it will be assumedthat the complex channel response estimate H_(ij) is represented by thecomplex channel gain from transmitting node j to receiving node i. Thefrequency domain representation is primarily suitable for OFDMA(Orthogonal Frequency Division Multiple Access), where each subcarriercan be treated according to the formulation above. However, theinvention is not limited to frequency domain handling of joint detectionof multiple signals, but can also be accomplished in the time-domain,yet generally with increased complexity when significant inter symbolinterference (ISI) exist. The time domain signal is somewhat morecomplicated, when inter symbol interference exist, but may then bewritten as:

${{R_{n}(v)} = {{\sum\limits_{m = 1}^{M}{H_{nm}*S_{m}}} + {N_{n}(v)}}},{n = 1},\ldots\mspace{11mu},N,$where ν is a time index (e.g. assuming sampling with the same rate asthe symbol rate).

The former frequency domain formulation for uplink signal processingexpression may simply be expressed as:R=H·S+N,where R is a vector representation of the soft complex information, H isa N by M complex channel gain matrix, S is a vector representation ofthe transmitted signals and N is a vector representation of whitecomplex Gaussian noise.

In practice, an estimate Ĥ of the complex channel gain matrix may bedetermined by explicit channel estimation, and the transmitted signalsare then detected based on the determined channel matrix, using anygeneral detection algorithm such as Zero Forcing (ZF), MaximumLikelihood Detection-Multi-User Detection (MLD-MUD) and Linear MinimumMean Squared Error (LMMSE).

For Zero Forcing (ZF) detection, an estimate Ŝ of the transmitted signalvector can be found as:Ŝ=Ĥ ⁻¹·R·

While using zero forcing equalizing on a system wide channel matrix maylead to noise amplification, it should be understood that this may becompensated for by a power control strategy that takes such factors intoaccount.

For Maximum Likelihood Detection-Multi-User Detection (MLD-MUD), anestimate Ŝ of the transmitted signal vector can be found as:

$\hat{S} = {\arg\mspace{11mu}{\min\limits_{\forall\; S}\left( {{R - {H \cdot \overset{\sim}{S}}}}^{2} \right)}}$where {tilde over (S)} is a hypothesis of the vector of transmittedsignals. Each element in the vector is generally taken from a modulationalphabet. However, the hypothesis {tilde over (S)} can be extended suchthat each element in {tilde over (S)} is a sequence of coded information(a codeword). It is then the task not just to find the most likelytransmitted symbols, but rather to find the most likely transmittedsequences. While this is generally very complex for long sequences,fairly short sequences should be possible to handle. In the relationabove, the norm is determined over the entire sequence, i.e. a searchfor the valid codewords that minimized the residual error energy.Alternatively, advances in multi-user decoding structures employing oneor more antennas can be integrated in the future. Possible forward errorcorrection coding schemes for the coded sequences are, but not limitedto, block codes, Trellis codes, Turbo codes and so forth.

Moreover, different combinations of complex channel gain matrix andsymbol hypothesis vector may be tested to find an optimal symbolhypothesis vector that then defines the detected signal information.This means that we can tune both the complex channel gain matrix and thehypothesis vector of the transmitted signals until an optimalcombination is found. By way of example, for MLD-MUD detection, this canbe expressed in the following way:

$\arg\mspace{11mu}{\max\limits_{{\forall\;\overset{\sim}{S}},{\forall\;\overset{\sim}{H}}}{\left( {{R - {\overset{\sim}{H} \cdot \overset{\sim}{S}}}}^{2} \right).}}$

In a sense, this means that the channel estimation forms part of thejoint detection process. If sequences are detected, the channel matrixcan also be allowed to vary slowly over the sequence duration.

The collected soft signal information generally retains phase andamplitude information from multiple receiving nodes/base stations.Normally, each receiving node/base station converts the receivedsuperposition of signals into digitized soft baseband signalsrepresented by complex samples. The complex samples can always berepresented by a real and imaginary component, or equivalently, byamplitude and phase. Soft information generally has a higher informationcontent than the finally detected or decoded information, and is usuallyrepresented by multiple (often binary) digits per signal component. Ifdesired, the soft information may include so-called probability orreliability information, such as received power level or otherinformation indicating the reliability of the information.

The overall flow of an exemplary multi-sensor processing procedure formulti-user detection according to a preferred example embodiment willnow be summarized with reference to FIG. 5. In act S1, each of a numberof receiving nodes (base stations) converts a superposition of receivedsignals into soft complex information, such as digitized complexbaseband signals. In act S2, complex baseband signals or similar softsignals are collected from the receiving nodes. In act S3, the complexchannel gain matrix between the receiving nodes and the transmittingnodes is typically determined, e.g. by explicit channel estimation orbased on the collected complex baseband information. In act S4, jointdetection of signal information, such as symbols or sequences (codewords), from multiple transmitting nodes is performed, preferably basedon the collected soft complex information and the estimated complexchannel gain matrix. As previously mentioned acts S3 and S4 may beintegrated and performed jointly.

FIG. 6 is a schematic block diagram illustrating an example of apreferred realization for multi-sensor processing according to thetechnology disclosed herein, implemented in a cellular radio network.Consider a number of mobile terminals 10-1, . . . , 10-M, each of whichtransmits a radio signal representing digital information to a number ofbase stations 120-1, . . . , 120-N. Each base station typically includestraditional base station equipment, such as a radio frequency section(RF) 122, a medium frequency section (MF) 124 and an analog/digitalconverter (AID) 126. Although the base station is illustrated as havinga single receiving antenna, there is nothing that prevents the basestation from using an advanced multi-antenna system. In this exemplaryembodiment it is assumed that the received signals are quadratureamplitude modulated (QAM), for example 64 QAM. This means that the A/Dconverter 126 will produce a digital baseband signal including bothin-phase (I) and quadrature-phase (Q) components, each with a resolutionof, for example, 10-15 bits (fewer or more bits are possible). In thisembodiment, these I and Q components represent soft information to besent to a central decoding node 130, for example an RNC, BSC or SHOD.The soft information is forwarded to an encapsulating unit 128, whichputs the information into packets suitable for transfer to the RNC/BSC130 over a transport network. At the RNC/BSC 130, the soft informationfrom the base stations 120-1, . . . , 120-N is received by one or moredecapsulating units 132, which retrieve the soft information. The soft Iand Q components from the base stations are then forwarded to adetection and decoding unit 134, which jointly detects the transmittedsignals from the mobile terminals 10-1, . . . , 10-M and subsequentlydecodes the detected signals. Alternatively, as mentioned earlier,decoding may be performed as an integrated part of the overall jointdetection process.

FIG. 7 is a schematic diagram illustrating an example of detection anddecoding unit according to the technology disclosed herein. In thisparticular example, the detection and decoding unit 134 comprises amodule 135 for determining a complex channel gain matrix, a jointdetection module 136 and a decoding module 137. The detection anddecoding unit 134 receives soft complex baseband signals from multiplereceiving nodes such as base stations. For example, the complex softbaseband signals may include I and Q components (or other softinformation indicative of reliability) from multiple base stations. TheI and Q components are transferred to the channel gain matrixdetermination module 135 for estimating respective complex channel gainestimates over one or more samples (e.g. over an entire frame) by meansof conventional channel estimation techniques. The complex channel gainestimates are normally determined simultaneously, per base station orfor all base stations at once, in a search procedure. The estimatedcomplex channel gain matrix is forwarded to the detection module 136,which based on this complex channel gain matrix and the soft I and Qcomponents jointly detects symbol information from the mobile terminals.Alternatively, each base station determines respective complex channelestimates related to the transmitting mobile terminals, and sendschannel estimation symbols in the soft information to the central node.More information on multi-user channel estimation techniques can befound, e.g. in references [9, 10]. Once detected, the retrieved symbolsare transferred to the decoding module 137, which performs decoding suchas channel decoding/error correction decoding and/or source decoding togenerated decoded data. While multi-user detection may be performed onsymbols and subsequent per-user decoding is performed, one may alsoperform multi-user detection on sequences equivalent to multi-userdecoding. Performing decoding as an integrated part of the detectionprocess implies that the detection module 136 may be configured forjoint detection and decoding, and that a separate decoding unit 137 maybe omitted.

FIG. 8 is a schematic diagram illustrating an exemplary architecture andsignal processing approach according to an alternative exampleembodiment. In similarity to the example of FIG. 4, the networkcomprises a plurality of receiving nodes 120-1, 120-2, 120-3 such asbase stations and a plurality of transmitting nodes 10 such as mobileterminals. Each receiving node 120 converts a superposition of signalsreceived from a plurality of transmitting nodes 10 to produce softcomplex signal information. In this embodiment, a number of receivingbase stations 120-1 and 120-3 transfer soft complex signal informationto a so-called controlling base station 120-2. The controlling basestation, which can be regarded as a “super base station”, takes its ownsoft complex information and combines it with the soft complexinformation received from the other base stations in a joint detectionprocess to detect the signal information from the transmitting mobileterminals.

In general, the receiving nodes are normally separate radio basestations. It should however be understood that it is possible that oneor more receiving nodes are remote radio units in a distributed radiobase station system, e.g. based on the concept of fiber-to-the-antenna(FTTA). In the latter case, analog/digital RF signals or IF signals maybe distributed from the remote units to the main unit of the distributedbase station system, in which digital baseband information from severalradio units may be extracted. The extracted digital baseband informationfrom one or several main units may then be transferred to a central nodesuch as the RNC node for signal detection and decoding in similarity tothe examples of FIGS. 4 and 6. Alternatively, however, the main unit ofsuch a distributed base station system is responsible for signaldetection and decoding, in similarity to the controlling base station inthe example of FIG. 8.

In the centralized approach, soft complex signal information iscollected from the considered receiving nodes and processed in a centralnode. Although optimal from a signal processing point of view, thecentralized multi-sensor processing approach may lead to somewhat highertransport costs for the network operators because of the large amountsof information that may have to be transported relatively longdistances.

Therefore, the technology disclosed herein also proposes a distributedapproach to the novel multi-sensor processing scheme. The distributedapproach is based on partitioning receiving nodes into multiple groups,and collecting soft complex signal information associated with thereceiving nodes of each group, and finally performing group-wise jointdetection based on the collected information. The receiving nodes may bepartitioned into groups based on e.g. geographical position orcorrelation characteristics. More particularly, on group level, thejoint detection is preferably performed based on the collected softcomplex signal information associated with the considered group and acomplex channel, representation such as a complex channel gain matrixrelated to the receiving nodes of the group and the relevanttransmitting nodes. The rationale behind this distributed approach isthat interference only has a limited meaning at very far distances, andhence it makes little sense to distribute soft information outside arather local neighborhood.

The problem associated with the transfer of large amounts of signal dataover the transport network has been analyzed in reference [8], in thecontext of per-user combining. However, the solution proposed inreference [8] implies that each base station should decode the signalreceived from a mobile and transfer a decoded signal to the centralexchange node, where the decoded signals are re-encoded, combined andfinally decoded. The technology disclosed herein, on the other hand,suggests a solution to this type of problem based on distributed jointmulti-user detection.

In a truly distributed realization, adjacent receiving nodes or basestations exchange soft complex signal information with each other, thusforming at least partially overlapping groups for distributed collectionof information, detection and decoding in each base station, asschematically illustrated in FIG. 9. The network of FIG. 9 comprises aplurality of receiving nodes 120-1, 120-2, 120-3, 120-4 such as basestations and a plurality of transmitting nodes 10 such as mobileterminals. Each base station 120 converts a superposition of signalsreceived from a plurality of transmitting nodes 10 to produce softcomplex signal information. In this example, the base stations 120-1,120-2, 120-3, 120-4 are partitioned into groups such that adjacent basestations form a number of at least partially overlapping groups. Thebase stations within a group exchange soft complex signal informationwith each other, and each base station then performs joint detection anddecoding of information from a number of mobile terminals 10. Thedecoding process may be performed separately, or as an integrated partof the detection process (sequence detection).

With the exchange of soft complex information illustrated in FIG. 9, thefollowing exemplary formulation for uplink signal processing would bepossible:

${\begin{bmatrix}R_{1} \\R_{2}\end{bmatrix} = {{\begin{bmatrix}H_{11} & H_{12} & H_{13} & H_{14} \\H_{21} & H_{22} & H_{23} & H_{24}\end{bmatrix} \cdot \begin{bmatrix}S_{1} \\S_{2} \\S_{3} \\S_{4}\end{bmatrix}} + {\begin{bmatrix}N_{1} \\N_{2}\end{bmatrix}\mspace{11mu}{in}\mspace{14mu}{base}\mspace{14mu}{station}\mspace{14mu} 120\text{-}1}}},{\begin{bmatrix}R_{1} \\R_{2} \\R_{3} \\R_{4}\end{bmatrix} = {{\begin{bmatrix}H_{11} & H_{12} & H_{13} & H_{14} \\H_{21} & H_{22} & H_{23} & H_{24} \\H_{31} & H_{32} & H_{33} & H_{32} \\H_{41} & H_{42} & H_{43} & H_{44}\end{bmatrix} \cdot \begin{bmatrix}S_{1} \\S_{2} \\S_{3} \\S_{4}\end{bmatrix}} + {\begin{bmatrix}N_{1} \\N_{2} \\N_{3} \\N_{4}\end{bmatrix}\mspace{11mu}{in}\mspace{14mu}{base}\mspace{14mu}{station}\mspace{14mu} 120\text{-}2}}},{\begin{bmatrix}R_{2} \\R_{3}\end{bmatrix} = {{\begin{bmatrix}H_{21} & H_{22} & H_{23} & H_{24} \\H_{31} & H_{32} & H_{33} & H_{34}\end{bmatrix} \cdot \begin{bmatrix}S_{1} \\S_{2} \\S_{3} \\S_{4}\end{bmatrix}} + {\begin{bmatrix}N_{2} \\N_{3}\end{bmatrix}\mspace{11mu}{in}\mspace{14mu}{base}\mspace{14mu}{station}\mspace{14mu} 120\text{-}3}}},{{{and}\begin{bmatrix}R_{2} \\R_{4}\end{bmatrix}} = {{\begin{bmatrix}H_{21} & H_{22} & H_{23} & H_{24} \\H_{41} & H_{42} & H_{43} & H_{44}\end{bmatrix} \cdot \begin{bmatrix}S_{1} \\S_{2} \\S_{3} \\S_{4}\end{bmatrix}} + {\begin{bmatrix}N_{2} \\N_{4}\end{bmatrix}\mspace{11mu}{in}\mspace{14mu}{base}\mspace{14mu}{station}\mspace{14mu} 120\text{-}4.}}}$

Based on the detected information, each base station may then performdecoding to generate decoded information, or alternatively, decoding isintegrated into the joint detection process. In order to avoid multiplecopies of the same decoded information to egress the network, decodedinformation may be transported from the base stations to a (hard)combining point 140 that combines the decoded information, e.g. byselection combining or majority combining. The combining point may beimplemented in a base station, a BSC/RNC or even a floating signalprocessing agent that follows a mobile terminal as it migrates.

The performance of the distributed approach will be asymptotically closeto the centralized multi-sensor processing even for relatively smallgroups involving just a few base stations, and also means that softinformation only have to be transported within a local neighborhood.Shorter transport distances in the transport network generally meansreduced costs for the operators.

Alternatively, the task of collecting soft complex information andperforming joint detection and optionally also decoding may be assignedto a signal processing node that is associated with the group. Such asignal processing node may of course be a designated base station thatbelongs to the corresponding group.

FIG. 10 illustrates an example of a distributed architecture and signalprocessing approach according to an alternative example embodiment, withsomewhat looser requirements on how the receiving nodes 120 (e.g. basestations) may be partitioned into groups. The groups may include notonly immediate neighbors, but also more distant neighbors. Stillhowever, some form of locality is desired so that soft information doesnot have to be exchanged/distributed from nodes situated very far fromeach. In the example of FIG. 10, three main groups A, B and C areformed. As mentioned above, some groups, here group A and group B, maybe associated with a designated signal processing node 130 that isresponsible for collecting soft complex information and performing therequired signal processing. In group B, a designated receiving node 120is responsible for collecting soft complex information and performingsignal processing. Decoded data from the three groups may be distributedto a so-called combining unit 140, which “combines” multiple copies ofthe same decoded data, thus performing some form of duplicate filtering.Higher layer protocols such as ARQ may be used after duplicatefiltering.

If it is not possible to directly detect all the relevant signalinformation from the considered mobile terminals, the technologydisclosed herein provides a procedure for performing iterative detectionof signal information based on distributed successive cancellation ofcurrently detected signal information from soft complex signalinformation.

FIG. 11 is a schematic diagram illustrating an example of the signalexchange in a distributed realization with optional successiveinterference cancellation according to a preferred example embodiment.

-   -   1. Each base station/cell receives a superposition of signals        from several mobile terminals, and generates corresponding soft        complex baseband information or other soft complex information.    -   2. Each base station/cell distributes soft complex baseband        information to one or more adjacent base stations/cells.    -   3. Each base station/cell jointly detects transmitted signals        from multiple mobile terminals by exploiting the exchanged soft        baseband information.

An optional extension to the above procedure involves the followingsteps:

-   -   4. Distribute detected signals and/or residual soft baseband        signals (where detected signals have been cancelled) to adjacent        base stations/cells.    -   5. Cancel detected signals from the residual baseband signals.        Normally, each base station/cell cancels received detected        signal information not previously available to the base station        from its residual soft baseband signal.

Repeat the successive cancellation until all (desirable) signals aredetected, or until a predetermined iteration limit is reached.

Alternatively, in a general approach, each base station may first try todetect signal information based on its own soft complex signalinformation before sending out residual soft baseband information wherethe detected information is cancelled. In other words, if a base stationdetects signal information from some of the mobile terminals, it maydetermine residual soft complex signal information by cancellation ofthe currently detected signal information. The collected soft complexinformation, including residual soft information, may then be used as abasis for detection until the signal information from all the consideredmobile terminals have been detected.

The whole iterative detection process may be seen as a range ofdetectors operating in parallel and exploiting distributed successiveinterference cancellation.

It has also been recognized that the amount of information that need tobe transported over the transport network can be significantly reducedby compressing soft complex signal information before it is transportedover the transport network and subsequently de-compressing thecompressed soft complex information so that it can be used in thedetection and decoding process.

FIG. 12 is a schematic block diagram illustrating a realization ofmulti-sensor processing including compression and de-compression of softinformation according to an exemplary embodiment. The block diagram ofFIG. 12 is similar to that of FIG. 6, except for the compression on thebase station side and the corresponding de-compression on the detectionand decoding side. By way of example, assume once again that the A/Dconverter 126 produces a digital baseband signal including both in-phase(I) and quadrature-phase (Q) components. Before these I and Q componentsare sent to the central RNC/BSC node, they are forwarded to a compressor127, which compresses the soft information. The compressed softinformation is forwarded to an encapsulating unit 128, which puts theinformation into packets suitable for transfer to the RNC/BSC 130 overthe transport network. At the RNC/BSC 130, the compressed informationfrom the base stations 120-1, . . . , 120-N is received by one or moredecapsulating units 132, which retrieve the compressed soft information.This compressed soft information is de-compressed in a set ofde-compressors 133, which at least approximately restore the I and Qcomponents originally sent from the respective base stations. Therestored I and Q components are then forwarded to the detection anddecoding unit 134.

The compression is typically lossy to obtain highest possiblecompression. This means that the de-compressed soft information may notbe exactly equal to the original soft information. Instead, it mayrepresent an approximation of this information. The compression should,however, be such that the de-compressed soft information contains moreinformation than the traditionally sent hard coded bits. It is alsoimportant that the compression retains phase and amplitude relationssuch that interference can be suppressed and signal-to-noise ratiomaximized.

A suitable compression method would be vector quantization of thecomplex values represented by the I and Q components. This vectorquantization may be performed on each I, Q pair. An alternative and moreefficient approach is to group several I, Q pairs into amulti-dimensional vector with complex-valued components, and performvector quantization of this multi-dimensional vector.

Vector quantization is a well known compression method that uses a table(often called a code book) of predetermined vectors. The quantization isaccomplished by comparing each vector in the table with the vector to bequantized The vector in the table with the smallest deviation from thedesired vector is selected. However, instead of sending the selectedvector itself, its table index is selected to represent the vector (thisis where the compression is obtained). The de-compressing end stores thesame table and retrieves the approximation vector by using the receivedindex to look it up in the table.

Although this aspect of the technology disclosed herein is illustratedfor a centralized architecture and signal processing approach, it isclear that each base station may be provided with a compressor as wellas a de-compressor to support compression/de-compression of soft complexsignal information also for distributed implementations.

For both centralized and distributed architectures, power control aswell as link mode control (including modulation, coding and spreading)can be adjusted to take advantage of the new signal processingarchitecture. In doing that, power control may also operate betweenmultiple base stations. FIG. 13 illustrates power control and link modefeedback in a system according to an exemplary embodiment. In a simplenetwork, soft complex information is collected for joint detection andsubsequent decoding. Various suitable quality indicators from thedetection unit 136 and/or an optional separate decoding unit 137 may betransferred to a radio resource management unit 138 for suitable powercontrol and/or link mode feedback to the mobile terminals 10. Intraditional power control schemes, the power control policy is to exceedany interfering signal with some margin. However, as the technologydisclosed herein strives to cancel interference by advanced multi-sensorprocessing, transmit power will rather be controlled with reference tothe noise floor. This change in power control objective may have animpact on the power control protocol, where power control decisions aretaken and power control PDUs are sent. The fact that power consumptionis reduced, since transmit power can be controlled with reference to thenoise floor, leads to even more efficient detection and decoding. Thisof course leads to even better power control settings, which in turnleads to even better interference cancellation and so on. Power controlcan be accomplished in several ways, e.g. through an inner power controlloop that compares instantaneous signal quality, such as signal tointerference (and noise) ratio with a target value, Γ. By adaptingtransmit power rapidly, any degradation in signal quality due to fastfading can be counteracted. Power control can also be performed on aslower basis with reference to an average power level. Outer loop powercontrol may derive its input from packet error rate or block error ratefigures, and adjust the signal to interference ratio target in responseto fulfill desired performance criteria for each link. The power controlcan, similarly to existing cellular systems, operate in a distributedfashion, i.e. each link is individually controlled, or alternatively apartially or fully centralized method may be adopted.

The embodiments described above are merely given as examples, and itshould be understood that the present invention is not limited thereto.Further modifications, changes and improvements which retain the basicunderlying principles disclosed and claimed herein are within the scopeof the invention.

REFERENCES

-   [1] Construction of Equivalent Scalar Channels for Orthogonal    Space-Time Coding, by Cheng Chang, Wei Wei.-   [2] Space-Time Coding, by Shingwa G. Wong, Michael P. Fitz, Aug. 19,    2003-   [3] U.S. Pat. No. 6,452,981, Sep. 17, 2002-   [4] W-CDMA Mobile Communications System, edited by Keiji Tachikawa,    Wiley & Sons, 2002, pp. 56-59, 66-73.-   [5] An Uplink Protocol Implementation in a Virtual Cellular Network,    by J. D. Bakker, R. Prasad-   [6] LMMSE Receivers performance under Non-Ideal Conditions, by    Lorenzo Mucchi, 2002.-   [7] Soft Detection and Decoding in Wideband CDMA Systems, by Kimmo    Kettunen, March 2003.-   [8] U.S. Pat. No. 6,320,852, Nov. 20, 2001-   [9] U.S. Pat. No. 6,445,342, Sep. 3, 2003-   [10] U.S. Pat. No. 6,640,088, Oct. 28, 2003

1. A method for detecting signal information in a wireless communicationnetwork having a number of nodes for communication, said methodcomprising: each of a plurality of receiving nodes converting asuperposition of signals received from a plurality of transmitting nodesto produce soft complex signal information, said plurality of receivingnodes being partitioned into multiple groups; collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network; jointly detecting said signal information inthe wireless communication network from at least a subset of saidplurality of transmitting nodes based on the collected soft complexsignal information; wherein said collecting soft complex signalinformation comprises collecting, for each group, soft complex signalinformation associated with the receiving nodes of the group, and saidjointly detecting comprises performing, for each group. joint detectionof signal information based on the collected soft complex signalinformation associated with the group; wherein said collecting, for eachgroup, soft complex signal information associated with the receivingnodes of the group comprises exchanging soft complex signal informationbetween the receiving nodes of the group.
 2. The method according toclaim 1, wherein said jointly detecting signal information from at leasta subset of said plurality of transmitting nodes is further based on acomplex channel representation related to said plurality of receivingnodes and said plurality of transmitting nodes.
 3. The method accordingto claim 2, wherein said complex channel representation is a complexchannel gain matrix.
 4. The method according to claim 3, wherein saidsoft complex signal information retains phase and amplitude information.5. The method according to claim 1, wherein said soft complex signalinformation is collected from said plurality of receiving nodes in acentral node, and said jointly detecting signal information is performedby the central node.
 6. The method according to claim 1, whereinperforming, for each group, joint detection of signal information isfurther based on a complex channel representation related to thereceiving nodes of the group and at least a subset of said plurality oftransmitting nodes.
 7. The method according to claim 1, wherein at leasttwo of said multiple groups are partially overlapping.
 8. The methodaccording to claim 1, wherein each group comprises a number of adjacentreceiving nodes.
 9. The method according to claim 8, wherein each of theadjacent receiving nodes within a group performs joint detection ofsignal information transmitted from at least a subset of said pluralityof transmitting nodes based on exchanged soft complex signalinformation.
 10. The method according to claim 1, wherein saidperforming, for each group, joint detection is performed by a signalprocessing node associated with the group of receiving nodes.
 11. Themethod according to claim 10, wherein said signal processing node is adesignated receiving node that belongs to the corresponding group.
 12. Amethod for detecting signal information in a wireless communicationnetwork having a number of nodes for communication, said methodcomprising: each of a plurality of receiving nodes convening asuperposition of signals received from a plurality of transmitting nodesto produce soft complex signal information, said plurality of receivingnodes being partitioned into multiple groups; collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network: jointly detecting said signal information inthe wireless communication network from at least a subset of saidplurality of transmitting nodes based on the collected soft complexsignal information; wherein said collecting soft complex signalinformation comprises collecting, for each group, soft complex signalinformation associated with the receiving nodes of the group, and saidjointly detecting comprises performing, for each group, joint detectionof signal information based on the collected soft complex signalinformation associated with the group: further comprising the steps of:generating, for each group, decoded signal information; transporting,for each group, corresponding decoded signal information to a combiningpoint for combining multiple copies of the same decoded signalinformation.
 13. A method for detecting signal information in a wirelesscommunication network having a number of nodes for communication, saidmethod comprising: each of a plurality of receiving nodes converting asuperposition of signals received from a plurality of transmitting nodesto produce soft complex signal information, said plurality of receivingnodes being partitioned into multiple groups: collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network: jointly detecting said signal information inthe wireless communication network from at least a subset of saidplurality of transmitting nodes based on the collected soft complexsignal information; wherein said collecting soft complex signalinformation comprises collecting, for each group, soft complex signalinformation associated with the receiving nodes of the group, and saidjointly detecting comprises performing, for each group, joint detectionof signal information based on the collected soft complex signalinformation associated with the group: further comprising performingiterative detection of signal information in the wireless communicationnetwork based on distributed successive cancellation of currentlydetected signal information from soft complex signal information.
 14. Amethod for detecting signal information in a wireless communicationnetwork having a number of nodes for communication, said methodcomprising: each of a plurality of receiving nodes converting asuperposition of signals received from a plurality of transmitting nodesto produce soft complex signal information; collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network; jointly detecting said signal information inthe wireless communication network from at least a subset of saidplurality of transmitting nodes based on the collected soft complexsignal information; each receiving node attempting to detect signalinformation based on its own soft complex signal information and, ifdetection of signal information from at least a subset of saidtransmitting nodes is successful, determining residual soft complexsignal information after cancellation of currently detected signalinformation; collecting residual soft complex signal information andcurrently detected signal information; and jointly detecting signalinformation based on the collected residual soft complex signalinformation and currently detected signal information.
 15. A system fordetecting signal information in a wireless communication network havinga number of nodes for communication, said system comprising: a pluralityof receiving nodes, each configured for converting a superposition ofsignals received from a plurality of transmitting nodes to produce softcomplex signal information, said plurality of receiving nodes beingpartitioned into multiple groups; means for collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network; and means for jointly detecting said signalinformation in the wireless communication network from at least a subsetof said plurality of transmitting nodes based on the collected softcomplex signal information: wherein said means for collecting softcomplex signal information comprises means for collecting, for eachgroup, soft complex signal information associated with the receivingnodes of the group; wherein said means for jointly detecting comprisesmeans for performing, for each group, joint detection based on thecollected soft complex signal information associated with the group; andwherein said means for collecting, for each group, soft complex signalinformation associated with the receiving nodes of the group comprisesmeans for exchanging soft complex signal information between thereceiving nodes of the group.
 16. The method according to claim 1,further comprising the steps of: compressing soft complex signalinformation on a receiving node side; collecting the compressed softcomplex signal information over a transport network; and decompressingthe compressed soft complex information before jointly detecting signalinformation.
 17. The system according to claim 15, wherein said meansfor jointly detecting is configured to operate based on the collectedsoft complex signal information in combination with a complex channelrepresentation related to said plurality of receiving nodes and saidplurality of transmitting nodes.
 18. The system according to claim 17,wherein said complex channel representation is a complex channel gainmatrix.
 19. The system according to claim 15, wherein said soft complexsignal information retains phase and amplitude information.
 20. Thesystem according to claim 15, wherein said soft complex signalinformation is collected from said plurality of receiving nodes in acentral node, and said means for jointly detecting signal information isimplemented in the central node.
 21. The system according to claim 15,wherein said wireless communication network is a cellular network, andsaid plurality of receiving nodes are base stations and said pluralityof transmitting nodes are mobile stations.
 22. The system according toclaim 15, wherein said means for performing, for each group, jointdetection is configured to operate based on the collected soft complexsignal information associated with the group and a complex channelrepresentation related to the receiving nodes of the group and at leasta subset of said plurality of transmitting nodes.
 23. The systemaccording to claim 15, wherein at least two of said multiple groups arepartially overlapping.
 24. The system according to claim 15, whereineach group comprises a number of adjacent receiving nodes.
 25. Thesystem according to claim 24, wherein each of the adjacent receivingnodes within a group performs joint detection of signal informationtransmitted from at least a subset of said plurality of transmittingnodes based on exchanged soft complex signal information.
 26. The systemaccording to claim 15, wherein said means for performing, for eachgroup, joint detection is implemented in a signal processing nodeassociated with the group of receiving nodes.
 27. The system accordingto claim 26, wherein said signal processing node is a designatedreceiving node that belongs to the corresponding group.
 28. The systemaccording to claim 15, further comprising: means for compressing softcomplex signal information on the receiving node side; means forcollecting the compressed soft complex signal information over atransport network; and means for decompressing the compressed softcomplex information for input of decompressed soft complex informationto said means for jointly detecting signal information.
 29. A system fordetecting signal information in a wireless communication network havinga number of nodes for communication, said system comprising: a pluralityof receiving nodes, each configured for converting a superposition ofsignals received from a plurality of transmitting nodes to produce softcomplex signal information, said plurality of receiving nodes beingpartitioned into multiple groups: means for collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network; and means for jointly detecting said signalinformation in the wireless communication network from at least a subsetof said plurality of transmitting nodes based on the collected softcomplex signal information; wherein said means for collecting softcomplex signal information comprises means for collecting, for eachgroup, soft complex signal information associated with the receivingnodes of the group; wherein said means for jointly detecting comprisesmeans for performing, for each group, joint detection based on thecollected soft complex signal information associated with the group; andfurther comprising: means for generating, for each group, decoded signalinformation; and means for transporting, for each group, correspondingdecoded signal information to a combining unit for combining multiplecopies of the same decoded signal information.
 30. A system fordetecting signal information in a wireless communication network havinga number of nodes for communication, said system comprising: a pluralityof receiving nodes, each configured for converting a superposition ofsignals received from a plurality of transmitting nodes to produce softcomplex signal information, said plurality of receiving nodes beingpartitioned into multiple groups; means for collecting soft complexsignal information associated with said plurality of receiving nodesover a transport network; and means for jointly detecting said signalinformation in the wireless communication network from at least a subsetof said plurality of transmitting nodes based on the collected softcomplex signal information; wherein said means for collecting softcomplex signal information comprises means for collecting, for eachgroup, soft complex signal information associated with the receivingnodes of the group; wherein said means for jointly detecting comprisesmeans for performing, for each group, joint detection based on thecollected soft complex signal information associated with the group: andfurther comprising means for performing iterative detection of signalinformation in the wireless communication network based on distributedsuccessive cancellation of currently detected signal information fromsoft complex signal information.
 31. A system for detecting signalinformation in a wireless communication network having a number of nodesfor communication, said system comprising: a plurality of receivingnodes, each configured for converting a superposition of signalsreceived from a plurality of transmitting nodes to produce soft complexsignal information; means for collecting soft complex signal informationassociated with said plurality of receiving nodes over a transportnetwork; and means for jointly detecting said signal information in thewireless communication network from at least a subset of said pluralityof transmitting nodes based on the collected soft complex signalinformation; means, in each receiving node, for attempting to detectsignal information based on its own soft complex signal information andfor determining, if detection of signal information from at least asubset of said transmitting nodes is successful, residual soft complexsignal information after cancellation of currently detected signalinformation; means for collecting residual soft complex signalinformation and currently detected signal information; and means forjointly detecting signal information based on the collected residualsoft complex signal information and currently detected signalinformation.